• DocumentCode
    3316078
  • Title

    Identification of IM Resistance Using Artificial Neural Network in Low Speed Region

  • Author

    Sönmez, Murat ; Yakut, Mehmet

  • fYear
    2007
  • fDate
    3-6 Dec. 2007
  • Firstpage
    437
  • Lastpage
    442
  • Abstract
    This paper presents a new method of estimation for the stator and rotor resistances of the induction motor for speed sensorless motor control drives, using artificial neural networks. The error between the motor quantity based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the stator resistance estimation. For the rotor resistance estimation, the error between the measured stator current and the estimated stator resistance using neural network is back propagated to adjust the weights of the neural network. The rotor speed is extracted from the induction motor state equations. The performance of the stator and rotor resistance estimators are investigated with the help of measured the stator voltage and current. Both resistances are estimated experimentally, using the proposed neural network in an induction motor drive.
  • Keywords
    backpropagation; electric current measurement; electric machine analysis computing; induction motor drives; neural nets; voltage measurement; IM resistance identification; artificial neural network; back propagation; current measurement; induction motor; rotor resistance estimation; speed sensorless motor control drives; stator estimation; voltage measurement; Artificial neural networks; Current measurement; Electrical resistance measurement; Estimation error; Induction motors; Motor drives; Neural networks; Rotors; Stators; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007. 3rd International Conference on
  • Conference_Location
    Melbourne, Qld.
  • Print_ISBN
    978-1-4244-1501-4
  • Electronic_ISBN
    978-1-4244-1502-1
  • Type

    conf

  • DOI
    10.1109/ISSNIP.2007.4496883
  • Filename
    4496883